Column

About

This dashboard shows results from the Berkeley Interpersonal Contact Study (BICS) in Spring 2020.

Caveats / underway:

  • except where noted, these results show the national and city samples pooled together

  • the values presented here have not yet been adjusted to improve sample representativeness

If you have questions or are interested in funding this study, please contact us at .

Initial support provided by a Berkeley Population Center pilot grant (NICHD P2CHD073964). This project has been approved by the UC Berkeley IRB (Protocol 2020-03-13128).

Number of interviews by week

Column

Number of conversational contacts

Number of conversational contacts

Number of conversational contacts outside the household

Conversational contacts by age

Conversational contacts outside household by age

Mixing

NB: please see the ‘data’ tab if you want the numbers behind these mixing estimates

By age/sex

By age/sex - non-hh contacts

Comparison - all conversational contacts

# A tibble: 16 x 6
# Groups:   ego_age [4]
   ego_age  alter_age  bics    fb ratio frac_decrease
   <chr>    <chr>     <dbl> <dbl> <dbl>         <dbl>
 1 [25,35)  [25,35)   1.09  7.16  0.152         0.848
 2 [25,35)  [35,45)   0.5   2.36  0.212         0.788
 3 [25,35)  [45,65)   0.451 1.25  0.361         0.639
 4 [25,35)  [65,100]  0.112 0.173 0.646         0.354
 5 [35,45)  [25,35)   0.513 3.29  0.156         0.844
 6 [35,45)  [35,45)   1.29  5.87  0.220         0.780
 7 [35,45)  [45,65)   0.367 1.70  0.215         0.785
 8 [35,45)  [65,100]  0.202 0.528 0.383         0.617
 9 [45,65)  [25,35)   0.317 2.23  0.142         0.858
10 [45,65)  [35,45)   0.411 3.10  0.132         0.868
11 [45,65)  [45,65)   0.943 3.77  0.250         0.750
12 [45,65)  [65,100]  0.319 0.755 0.422         0.578
13 [65,100] [25,35)   0.222 0.737 0.301         0.699
14 [65,100] [35,45)   0.322 2.14  0.150         0.850
15 [65,100] [45,65)   0.451 2.00  0.225         0.775
16 [65,100] [65,100]  0.791 2.17  0.364         0.636

Relationships

Relationships, non-household contacts

Locations

Locations - non-household contacts

Contact durations - by relationship

Contact durations - by respondent age

COVID-19

Awareness

Concern

Behavior change

Cities

Number of interviews

Conversational contacts

Conversational contacts outside of household

Model

Overview

To help summarize patterns in the contact survey data, we fit a negative binomial model, accounting for the right-censoring of reported contacts at 10. These models show relationships among people who have completed the survey; have not been adjusted in any way for sampling. We fit these models using the brms package in R.

We modeled the expected log number of reported contacts as a function of age group, city, gender, and household size. The plots below show posterior means and 95% credible intervals for the estimated coefficients. Estimated coefficients greater than 0 imply that the predictor is associated with higher reported numbers of contacts, while estimated coefficients less than 0 imply that the predictor is associated with lower reported numbers of contacts.

There are two models: one for total number of contacts, and one for the number of non-household contacts.

Negative-binomial model for household contacts

Negative-binomial model for non-household contacts

Technical details

Coming soon.

Respondent characteristics

Age/sex

Race/ethnicity

Household size

Contact definition

Respondents to the survey were told to consider someone a contact using this text:

We would like to ask you some questions about people you had in-person conversational contact with yesterday.

By in-person conversational contact, we mean a two-way conversation with three or more words in the physical presence of another person.

You might have conversational contact with family members, friends, co-workers, store clerks, bus drivers, and so forth.

(Please do not count people you contacted exclusively by telephone, text, or online. Only consider people you interacted with face-to-face.)

 

Data

Microdata

We plan to make a version of the data with no identifying information publicly available as soon as we can. If you are a disease modeler who urgently needs to see the microdata, please reach out to us by email.

The estimated mixing matrices are reproduced as tables below. Note that these are the crude estimates, and have not had a symmetry constraint enforced.

All contact mixing estimates

ego_age alter_age weighted_n raw_n num_interviews avg_per_ego
[18,25) [0,10) 35.66667 25 185 0.1927928
[18,25) [10,18) 68.75000 51 185 0.3716216
[18,25) [18,25) 185.16667 154 185 1.0009009
[18,25) [25,35) 81.00000 65 185 0.4378378
[18,25) [35,45) 62.41667 49 185 0.3373874
[18,25) [45,65) 110.75000 93 185 0.5986486
[18,25) [65,100] 10.25000 9 185 0.0554054
[25,35) [0,10) 74.33333 60 279 0.2664277
[25,35) [10,18) 39.66667 29 279 0.1421744
[25,35) [18,25) 72.33333 59 279 0.2592593
[25,35) [25,35) 304.25000 243 279 1.0905018
[25,35) [35,45) 139.50000 104 279 0.5000000
[25,35) [45,65) 125.75000 104 279 0.4507168
[25,35) [65,100] 31.16667 26 279 0.1117085
[35,45) [0,10) 69.08333 57 298 0.2318233
[35,45) [10,18) 98.25000 75 298 0.3296980
[35,45) [18,25) 47.00000 37 298 0.1577181
[35,45) [25,35) 152.75000 117 298 0.5125839
[35,45) [35,45) 384.33333 299 298 1.2897092
[35,45) [45,65) 109.25000 92 298 0.3666107
[35,45) [65,100] 60.33333 54 298 0.2024609
[45,65) [0,10) 51.41667 32 450 0.1142593
[45,65) [10,18) 115.00000 90 450 0.2555556
[45,65) [18,25) 96.25000 79 450 0.2138889
[45,65) [25,35) 142.58333 122 450 0.3168519
[45,65) [35,45) 185.08333 154 450 0.4112963
[45,65) [45,65) 424.25000 357 450 0.9427778
[45,65) [65,100] 143.41667 130 450 0.3187037
[65,100] [0,10) 13.50000 12 213 0.0633803
[65,100] [10,18) 10.91667 10 213 0.0512520
[65,100] [18,25) 23.16667 21 213 0.1087637
[65,100] [25,35) 47.25000 44 213 0.2218310
[65,100] [35,45) 68.66667 59 213 0.3223787
[65,100] [45,65) 96.00000 83 213 0.4507042
[65,100] [65,100] 168.50000 148 213 0.7910798

Non-household mixing estimates

ego_age alter_age weighted_n raw_n num_interviews avg_per_ego
[18,25) [0,10) 10.500000 8 185 0.0567568
[18,25) [10,18) 12.500000 11 185 0.0675676
[18,25) [18,25) 73.416667 61 185 0.3968468
[18,25) [25,35) 37.833333 29 185 0.2045045
[18,25) [35,45) 15.166667 13 185 0.0819820
[18,25) [45,65) 9.916667 9 185 0.0536036
[18,25) [65,100] 5.250000 4 185 0.0283784
[25,35) [0,10) 4.000000 4 279 0.0143369
[25,35) [10,18) 5.250000 4 279 0.0188172
[25,35) [18,25) 30.250000 25 279 0.1084229
[25,35) [25,35) 123.250000 96 279 0.4417563
[25,35) [35,45) 52.833333 42 279 0.1893668
[25,35) [45,65) 52.250000 40 279 0.1872760
[25,35) [65,100] 16.833333 12 279 0.0603345
[35,45) [0,10) 3.750000 3 298 0.0125839
[35,45) [10,18) 5.000000 4 298 0.0167785
[35,45) [18,25) 27.083333 21 298 0.0908837
[35,45) [25,35) 73.750000 55 298 0.2474832
[35,45) [35,45) 100.083333 83 298 0.3358501
[35,45) [45,65) 59.166667 50 298 0.1985459
[35,45) [65,100] 23.083333 20 298 0.0774609
[45,65) [0,10) 7.250000 5 450 0.0161111
[45,65) [10,18) 5.750000 5 450 0.0127778
[45,65) [18,25) 27.000000 22 450 0.0600000
[45,65) [25,35) 83.416667 73 450 0.1853704
[45,65) [35,45) 105.416667 90 450 0.2342593
[45,65) [45,65) 150.166667 128 450 0.3337037
[45,65) [65,100] 66.000000 59 450 0.1466667
[65,100] [0,10) 8.500000 8 213 0.0399061
[65,100] [10,18) 7.916667 7 213 0.0371674
[65,100] [18,25) 10.000000 9 213 0.0469484
[65,100] [25,35) 34.000000 32 213 0.1596244
[65,100] [35,45) 43.583333 36 213 0.2046166
[65,100] [45,65) 54.500000 46 213 0.2558685
[65,100] [65,100] 54.750000 49 213 0.2570423